Simulation of early COVID-19 using SIR model and variants (SEIR ...).

Overview

COVID-19-simulation

Simulation of early COVID-19 using SIR model and variants (SEIR ...). Made by the Laboratory of Sustainable Life Assessment (GYRO) of the Federal Technologycal University - Parana (UTFPR-ct) in the scope of the project GYRO4Life

Running the simulation

The code runs based on a csv with the same structure of nc85.csv or oa85.csv files which has a time series of confirmed cases and deaths and metadata information about the region being characterized on the line. Both cases and deaths have to be given for the simulation.

The main code is simulação.py, which receives a couple of arguments:

  • 1: region code (for the csv being used). In case the argument is empty ("-"), it will run for all lines of the csv [ex: -28]
  • 2: Name of the csv file with confirmed cases (omit the '.csv') [ex: nc85.csv -> -nc85]
  • 2: Name of the csv file with confirmed deaths (omit the '.csv') [ex: oa85.csv -> -oa85]
  • 3: Fitting method [-0: basinhopp, -1: differential evolution [default], -2: powell, -3: cobyla] [ex: -1]
  • 4: Boolean and quantity of opening and closure regimes for the simulation for confirmed cases (works as a contingency method reducing the probability of infection). '-0-0' ignores this factor for a simulation without contingency methods. If a quantity is given on the second argument, the boolean argument must be 1 [ex: '-1-1']
  • 5: Boolean and quantity of opening and closure regimes for the simulation for confirmed deaths (works as a contingency method reducing the probability of infection). '-0-0' ignores this factor for a simulation without contingency methods. If a quantity is given on the second argument, the boolean argument must be 1 [ex: '-1-1']
  • 6: Type of simulation [-n: simulation of one location (one csv line), -s: simulation of all csv locations, -b: bootstrap of one location [has uncertainty], -sl: simulation of a location with sensibility analysis] [ex: -n]
  • 7: Simulation period in days [ex: -200]
  • 8: number of days for validation [ex: -5]
  • 9: Subtype of simulation [-mod: hospitalization simulation, -std: SEIR simulation with asymptomatic and deaths]
  • 10: Run tests and additional graphics [-0: no, -1: yes]

Example call for a SEIR simulation with bootstrap using cases and deaths in Brazil. The simulation is done for 200 days and with a validation of 5 days.

python simulacao.py -28 -nc85 -oa85 -1 -1-2-0-0 -b -200 -5 -str -0
Owner
José Paulo Pereira das Dores Savioli
José Paulo Pereira das Dores Savioli
Python library for multilinear algebra and tensor factorizations

scikit-tensor is a Python module for multilinear algebra and tensor factorizations

Maximilian Nickel 394 Dec 09, 2022
Titanic Traveller Survivability Prediction

The aim of the mini project is predict whether or not a passenger survived based on attributes such as their age, sex, passenger class, where they embarked and more.

John Phillip 0 Jan 20, 2022
This repository has datasets containing information of Uber pickups in NYC from April 2014 to September 2014 and January to June 2015. data Analysis , virtualization and some insights are gathered here

uber-pickups-analysis Data Source: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city Information about data set The dataset contain

B DEVA DEEKSHITH 1 Nov 03, 2021
Python factor analysis library (PCA, CA, MCA, MFA, FAMD)

Prince is a library for doing factor analysis. This includes a variety of methods including principal component analysis (PCA) and correspondence anal

Max Halford 915 Dec 31, 2022
Flightfare-Prediction - It is a Flightfare Prediction Web Application Using Machine learning,Python and flask

Flight_fare-Prediction It is a Flight_fare Prediction Web Application Using Machine learning,Python and flask Using Machine leaning i have created a F

1 Dec 06, 2022
#30DaysOfStreamlit is a 30-day social challenge for you to build and deploy Streamlit apps.

30 Days Of Streamlit 🎈 This is the official repo of #30DaysOfStreamlit — a 30-day social challenge for you to learn, build and deploy Streamlit apps.

Streamlit 53 Jan 02, 2023
Time-series momentum for momentum investing strategy

Time-series-momentum Time-series momentum strategy. You can use the data_analysis.py file to find out the best trigger and window for a given asset an

Victor Caldeira 3 Jun 18, 2022
Data from "Datamodels: Predicting Predictions with Training Data"

Data from "Datamodels: Predicting Predictions with Training Data" Here we provid

Madry Lab 51 Dec 09, 2022
Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Auto_TS: Auto_TimeSeries Automatically build multiple Time Series models using a Single Line of Code. Now updated with Dask. Auto_timeseries is a comp

AutoViz and Auto_ViML 519 Jan 03, 2023
pymc-learn: Practical Probabilistic Machine Learning in Python

pymc-learn: Practical Probabilistic Machine Learning in Python Contents: Github repo What is pymc-learn? Quick Install Quick Start Index What is pymc-

pymc-learn 196 Dec 07, 2022
Hierarchical Time Series Forecasting using Prophet

htsprophet Hierarchical Time Series Forecasting using Prophet Credit to Rob J. Hyndman and research partners as much of the code was developed with th

Collin Rooney 131 Dec 02, 2022
DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning.

DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported ha

Microsoft 1.1k Jan 04, 2023
Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information.

Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information.

Priyansh Sharma 7 Nov 09, 2022
Self Organising Map (SOM) for clustering of atomistic samples through unsupervised learning.

Self Organising Map for Clustering of Atomistic Samples - V2 Description Self Organising Map (also known as Kohonen Network) implemented in Python for

Franco Aquistapace 0 Nov 16, 2021
Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining

**Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining.** S

Sebastian Raschka 4k Dec 30, 2022
A single Python file with some tools for visualizing machine learning in the terminal.

Machine Learning Visualization Tools A single Python file with some tools for visualizing machine learning in the terminal. This demo is composed of t

Bram Wasti 35 Dec 29, 2022
Datetimes for Humans™

Maya: Datetimes for Humans™ Datetimes are very frustrating to work with in Python, especially when dealing with different locales on different systems

Timo Furrer 3.4k Dec 28, 2022
AP1 Transcription Factor Binding Site Prediction

A machine learning project that predicted binding sites of AP1 transcription factor, using ChIP-Seq data and local DNA shape information.

1 Jan 21, 2022
Machine Learning University: Accelerated Natural Language Processing Class

Machine Learning University: Accelerated Natural Language Processing Class This repository contains slides, notebooks and datasets for the Machine Lea

AWS Samples 2k Jan 01, 2023
Python library which makes it possible to dynamically mask/anonymize data using JSON string or python dict rules in a PySpark environment.

pyspark-anonymizer Python library which makes it possible to dynamically mask/anonymize data using JSON string or python dict rules in a PySpark envir

6 Jun 30, 2022